Health Information Management - Science topic

Is there a specific view of security you are thinking of, or is this just a wide and general question?

Thinking of security with respect to network/cyber attacks, then Electroni Health Records simply inherit the same deep learning techniques that can be applied to any networked systems/applications (perhaps with some diffeent 'value' calclulations as to the monetary expression of the damage done in such an attack).

If this question is about privacy concerns and data use - who has access to and whwo uses your data, then mostly the access controlls and logging of data access are within the EHR applications themselves (EHR, personal health record or any system/application that looks after health information).

The are also in some networked environments with multiple actors, there are audit logs that can be inspected (the IHE ATNA being an example). I know of a few real world examples (but with no accademic publications) where such logs were inspected and the evidence of un-authorised access (or miss use of authorised access) was proven. Since this activity is looking for patterns in a log file (or set of log files with some correlation) then this looks to me exactly like the kind of problem that deeo learning could be applied to. Note "who" is looking at "what" data implies you need to know the identities of the "who" in a real world context that would confirm or deny that they are allowed to look at "whatever-it-is".

In private healthcare based economies, your insurance premiums are based on your declared health. Health insurance companies may use deep learning tools in analysis of their customers health records - but this is ouutside of my experience and it is of course linked to the legality of doing this (which I am surer varies from country to country).

Effectively though, this kind of deep learning would ook like a specialisation of the kind of deep learning applied to fraud (specialised for healthcare insurance premiums and delcarations).

I am sure there are other areras. Computer architecture that results in multiple copies of healthcare data items and records leads to ambiguity in records - where is the source of truth. You can see how in the above fraud example any ambiguity might lead to loopholes for fraudulent activity or put a consumer in a position where they find it difficult to prove their health records. I am not sure if deep learning would be applicable here, when inspection of the architeture would tell you about this potential-duplicates problem.

Your question seems quite wide, I hope my contribution is useful for you and I haven't missed the poiont of your question.

The question becomes the degree to which national economies are affected by disease burdens when the nature of that burden is unclear because of different standards of reportage. Similarly, if you mean data on supply--for instance ventilators or certain medications--if unreliable the limits of those records will result in a lack of resources at treatment sites. The effect on the economy in the first will involve the cost of unanticipated disease incidence or the expansion of diseases (for example in epidemic cases) that might have been anticipated, with better records, and thus better prepared for. Similarly, if it's about supply of equipments or pharmaceuticals the shortages that result from inconsistent records will mean either higher costs for emergency purchase or higher mortality/morbidity in the absence of necessary equipment or pharmaceuticals. If the numbers are sufficient--for instance provision of vaccines, or undercounting of those with critical conditions requiring extensive medication, the effect may be broadly economic as well as clnical.

As far as I conceive this question, it's for front line health workers who work in the health department. Their job profile is also to collect information/data in the community whose health is at stake. If they are illtreated then the quality of data will be poor and that effects planning and to imparting health services . So , it's essentially important to take care of these managers and in turn the quality information will be available in such settings.

I agree with Jorge Tavares

For high quality journals, see:

For the time of first decision and revision, check each of those journals' website. They may provide an estimate. Other than that, you can use journal finder tools like Elsevier's. Simply copy and paste your abstract and it will suggest you most suitable journals. In that list, it will also provide timeline for first decision and reviews, and acceptance rate:

Here is a starting point. I will be happy to upload specific paper on TOE.

I realize this is an old question, but since there are some followers to it I just want to point out that there is a project on RG with lots of references to DHIS2:

Most of our research concerns the implementation and use of DHIS2, so there should be a lot of relevant articles there to help answer the original question, or any question you would have around DHIS2.

The transition from one set of standards to a new set of standard follows the same principles every time. The standards organisations may publish guidance documents on the subject, if they don't for your particular standards, then you only need to follow the principles set-out in other examples. For example ISO, BSI and Australian Standards publish these; https://www.iso.org/iso/iso_9001_-_moving_from_2008_to_2015.pdf, https://www.bsigroup.com/LocalFiles/es-ES/.../ISO-9001-transition-guide.pdf  & https://www.saiglobal.com/en-au/quality_management_9001/transition_to_quality_management_systems_iso_9001_2015/ respectfully which provide very similar material. There is a bit of work to do, firstly there needs to be unconditional support from management and those responsible for paying the bills. Then you need to decide on a plan/timeframe (be realistic unless there is a deadline by which they MUST be implemented) to achieve the new standards, remember this may change as you get a better understanding of the differences between standards. That is where the unconditional support of management is very important. Now it is time to sit down and going through both standards in detail to find similarities and differences. There will be some items which map directly from one standard to another, of-course there will be completely new items and possibly even items which are no longer required.

Have a read through the links I provided and hopefully they will make sense and help you with your task.

Cheers

Lindsay

Diagnostic tools present a complex challenge in cost-effectiveness analyses.  In general, before you can start, you need to understand the value of the information provided by the new diagnostic tool, in terms of how it changes treatment decisions and outcomes.   Specifically:

Once you have modeled how your diagnostic tool changes treatment paths and outcomes -- and for whom -- you can apply costs/utilities in accordance with your analytical objectives.  It's a challenge, but if you walk though the logic you will see quickly that the value of a diagnostic tool is often governed by the effectiveness of the treatments that might be applied as a result of the new information. 

Thank you Sanden for taking the time to submit a contribution.

Perhaps clarification is needed about this issue that concerns me i.e. the use by nurses of what are called predictive assessments in which the goal is to PREVENT a fall falling by working though a format that ascribes a risk / probability value that is typically identified as a patient as being at low, medium or high risk of falling.

There are many straightforward hazard elimination and passive impact provision measures that will mitigate harm to patients from the falls; an outcome that cannot be achieved by the use of predictive assessments.  

These references, one of many credible sources, provide good background about the issue concerning predictive falls assessment tools:

It is not possible to use probability for single event management i.e. in this case preventing a fall by predicting who will fall and when. However and in spite of the obvious uselessness of predictive falls assessment tools, nurses across the UK and Ireland spend vast amounts of their time off-the-floor away from patients completing predict assessments.

The obvious risk statement and associated real-time helpful interventions are ignored:

Dear Prof. Oliveria,

First of all, I'm not a professional. But I think Information Quality Research has been very widely done and it depends on dimensions of particular data that's being processed or analyzed. Maybe this article sheds more light

regards

I spoke with my peer here who teaches this subject in our University.  She agreed with Stella's answer, tho says the learning environment is not always a hospital, but it is always involved in healthcare.  The site supervisor evaluates the student - and the student provides feedback on the site.

Hope this helps --- there doesn't seem to be any standard ---so perhaps that is an opportunity for you to develop one!!

I suggest you interrogate the Health Foundation (based in the United Kingdom) website-

A lot of effort has been put into quality and safety activities within NHS hospitals this last decade and one of the drivers have been the various well publicized incidents involving breaches of accepted standards of care within some of the hospitals.

This kind of question sometimes generated discussion when posted via Twitter.

This published paper in Journal of Medical Systems could be helpful:

Title: Privacy and Security in Mobile Health Apps: A Review and Recommendations

Link:

This is an interesting conversation about health literacy. I personally feel much of the responsibility for educating patients falls on the health care providers. There are many excellent tools to raise awareness of the issues and I though I would share a few here. Pfizer, the pharmaceutical company, has been a huge advocate for Clear Health Communication for years and I applaud their efforts. They have a new tool, A Health Literacy Assessment Tool for Patient Care and Research called the Newest Vital Sign (NVS) available in English & Spanish. It helps providers assess what the patient knows, and then using some of their clear health communication skills (in the link provided) we as health professionals can work to better communicate and get feedback on what the patient understands about their illness and any treatment options we are recommending.

The American Medical Association has a really powerful video on health literacy in America. Watch it to really see what we are missing by not looking for health literacy, it's been around for a while, but wow. (Every time I watch it I am moved by the impact of health literacy on patients.) We should never assume anything, plain language is important and it is not the patient's responsibility to tell us they "don't get it", because they will not. Providers need to think about this issue and I am glad it is being discussed here. There are great resources out there.

I am keen to know if the brief IPQ has been utilized with Cranial diabetes insipidus. Cranial Diabetes, as you would be aware is idiopathic. however the brain producing little or no anti-durect hormone can be the result of head injuries, pituitary tumours or nerosurgery or haemochromatosis and sarcoidosis, infections TB genetic defects(rarely), a variety of kidney conditions, inherited genetic disorders. given that conditions such as haemochromatosis often go undetected there may be individuals undiagnosed with cranial diabetes.

Lots of good thinking here, and, agree with Deborah's response as well -

I would also like to add that, the definition of the SLAs is probably an ideal place for a 'functional collaboration' between the strategic *and* operational information management teams, as well as key business stakeholders (including operational and strategy).

In this conversation - if it is facilitated in a successful manner - both sides of the information management team will gain a deeper appreciation of each other's mandate, key goals and purposes -

As well as an enhanced understanding of how they might better align with the key organizational and strategic goals overall.

(ESB=Enterprise Service Bus)

I suggest one way to validate a model or a classification system with a number of hypothesis within which questionnaire can be used to validated. Other approaches include simulation and experimental data. You might find this useful IEEE article http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=608984&tag=1

I don't have any particular insights, but it seems like the problem you are trying to tackle may be too large. It may behoove you to narrow your focus, such as creating a mathematical model to balance visibility of a particular area invasiveness. This certainly is not a simple problem, but seems it could easily be a factor in your grander model. I can appreciate wishing to hash out what factors might come into play though so you can tackle each piece individually. I would imagine even given a suggested incision it would ultimately be more a factor of the surgeon's skill level and even preference. Don't let this discourage you from trying to inform them.

As you can probably tell though, I have no expertise in the field. So weigh my considerations accordingly.